agents-flex
Agents-Flex is a Java-native, lightweight high-performance AI agent framework that unifies large models, image generation, TTS/STT, video generation, tool calling, RAG and agent orchestration to help teams build deployable multimodal AI applications.
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agents-flex
Agents-Flex 是一款面向 Java 开发者的轻量级、高性能 AI Agent 框架。它将大模型、图片生成、语音(TTS/STT)、视频生成、Tool Calling、RAG 与 Agent 编排等多模态能力通过统一的 Java 接口进行抽象与接入,目标是帮助团队更快构建可上线的多模态 AI 应用。框架提供从模型接入、工具与知识接入到多 Agent 协作、数据分析与生产可观测的一整套工程化栈,支持国产模型、云服务与私有化部署。
Java-based LLM application framework similar to LangChain.
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Claim this listing for $29Key Features
统一模型抽象
通过 ChatModel、ImageModel、VideoModel、EmbeddingModel、RerankModel 以及语音模型接口,统一接入不同厂商与私有化模型服务。
多模态生成
支持文本、图片(文生图、图生图、图片编辑)、语音 TTS(同步与流式)、STT 语音转写与视频生成,使用统一接口屏蔽不同服务商差异。
Agent 与工具编排
从 Java 方法到 MCP 工具、Skills、Subagent,支持 ToolScanner/Tool.Builder、MCP Client、ReAct、Routing 与 Subagent 等编排能力,让 Agent 调用业务能力并拆解任务。
知识与 RAG 支持
覆盖文档处理、Embedding、向量存储、检索、重排与 LLM Wiki(Loader / Parser / Splitter、Vector Store / SearchWrapper、LLM Wiki),支持扁平语义检索与层级知识导航。
生产级保障
提供高可用路由、重试熔断、调用链追踪与指标采集,集成 OpenTelemetry,并提供 Spring Boot 自动配置以便上线运行。
模型路由与流式响应
支持 HTTP / SSE / WebSocket 的同步与流式响应,并提供模型路由与标签选择能力以适配复杂部署场景。
生态与适配
适配中国生态与多家云与模型服务(页面列举 OpenAI / Stability / Gitee AI、阿里云 / 腾讯云 / 火山引擎 等实现示例)。
示例与快速上手
提供多模态示例代码(ChatModel、Image/TTS/STT/Video 的核心调用示例)和快速开始指南,支持通过 Maven 依赖引入。
Pricing
Open-source: Released under the Apache License
Use Cases
智能客服与聊天助手
构建基于大模型的客服与聊天机器人,结合 RAG 接入企业知识库以提供准确回复。
企业知识库与 RAG 问答
使用文档处理、Embedding、向量检索与 LLM Wiki 实现层级文档导航与问答能力。
语音助手与音频转写
集成 TTS/STT 实现实时语音交互、转写与音频生成,支持流式播放与低延迟体验。
营销素材与创意图片/视频生成
通过 ImageModel 与 VideoModel 统一生成图片与短视频,用于自动化素材生产与创意生成。
MCP 工具连接与自动化
将 Java 业务方法暴露为 Agent 可调用的工具,连接 MCP 工具以实现自动化任务执行与工作流编排。
多模型网关与高可用路由
作为多模型网关,提供路由、重试与熔断等生产级能力,保证多模型接入下的高可用性。
Integrations
OpenAI / Stability / Gitee AI
示例中列举的图片与聊天模型提供者,框架可通过 ImageModel/ChatModel 等接口接入这些服务。
阿里云 / 腾讯云 / 火山引擎
示例中用于 TTS/STT 和视频生成的云服务适配实现,页面列举这些实现示例。
Spring Boot
提供 Spring Boot 自动配置与 Starter,便于在 Spring 应用中集成并上生产。
OpenTelemetry
用于调用链追踪与可观测性集成,支持生产监控。
Benefits
Limitations
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Frequently Asked Questions
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Getting Started
- 1 Step 1: 接入模型 — 按场景配置 ChatModel、ImageModel、TextToSpeechModel 或 VideoModel(示例使用 OpenAI/Gitee/阿里云 配置)。
- 2 Step 2: 暴露工具 — 用注解或 Builder 将 Java 业务方法变成 Agent 可调用的 Tool/MCP 技能。
- 3 Step 3: 接入知识 — 组合 RAG、WebSearch、LLM Wiki 为回答提供外部上下文和文档支持。
- 4 Step 4: 编排 Agent — 使用 ReAct、Routing、Subagent 处理多步骤与多角色任务。
- 5 Step 5: 上线观测 — 接入路由、重试、熔断与 OpenTelemetry,使用 Spring Boot Starter 部署运行。
Support
docs
官网提供帮助文档与快速开始示例(页面导航包含“帮助文档”与“ChangeLog”)。
code repo
页面导航列出 Gitee,用于代码托管与贡献(Gitee 入口在站点导航)。
API
官网帮助文档与多模态示例代码(页面包含快速开始、图片/TTS/STT/视频示例及配置示例)。
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